Transforming Prior Authorization to Decision Support

2017 ◽  
Vol 13 (1) ◽  
pp. e57-e61 ◽  
Author(s):  
Lee N. Newcomer ◽  
Richard Weininger ◽  
Robert W. Carlson

Purpose: To evaluate a computer-based prior authorization system that was designed to include and test two new concepts for physician review: (1) the tool would minimize denials by providing real-time decision support with alternative options if the original request was noncompliant, and (2) the tool would collect sufficient information to create a patient registry. Methods: A new prior authorization tool incorporating real-time decision support was tested with a large national payer. The tool used the National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology as the content for decision making. Physicians were asked to submit the minimal amount of clinical data necessary to reach a treatment-decision node within the National Comprehensive Cancer Network Guidelines. To minimize denials, all available recommended treatments were displayed for physician consideration and immediate authorization was granted for any compliant selection. Results: During a 1-year pilot in a Florida commercial health plan, 4,272 eligible cases were reviewed with only 42 denials. Chemotherapy drug costs for the prior authorization pilot were compared with a similar time period in the previous year for the state of Florida, as well as for the Southeast region and for the nation, which served as controls. The percentage change between the time periods was −9% in Florida, 10% for the national costs, and 11% for the Southeast region costs. The difference between the regional increase and the Florida decrease represented a savings of $5.3 million dollars for the state of Florida in 1 year. Conclusion: There is significant opportunity to reduce the costs of therapy while being compliant with nationally accepted guidelines for cancer chemotherapy.

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 778-P
Author(s):  
ZIYU LIU ◽  
CHAOFAN WANG ◽  
XUEYING ZHENG ◽  
SIHUI LUO ◽  
DAIZHI YANG ◽  
...  

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1169
Author(s):  
Juan Bógalo ◽  
Pilar Poncela ◽  
Eva Senra

Real-time monitoring of the economy is based on activity indicators that show regular patterns such as trends, seasonality and business cycles. However, parametric and non-parametric methods for signal extraction produce revisions at the end of the sample, and the arrival of new data makes it difficult to assess the state of the economy. In this paper, we compare two signal extraction procedures: Circulant Singular Spectral Analysis, CiSSA, a non-parametric technique in which we can extract components associated with desired frequencies, and a parametric method based on ARIMA modelling. Through a set of simulations, we show that the magnitude of the revisions produced by CiSSA converges to zero quicker, and it is smaller than that of the alternative procedure.


Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.


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